rmse

EstimatorReport.metrics.rmse(*, data_source='test', multioutput='raw_values')

Compute the root mean squared error.

Parameters:

data_source{“test”, “train”}, default=”test”

The data source to use.

• “test” : use the test set provided when creating the report.

• “train” : use the train set provided when creating the report.

multioutput{“raw_values”, “uniform_average”} or array-like of shape (n_outputs,), default=”raw_values”

Defines aggregating of multiple output values. Array-like value defines weights used to average errors. The other possible values are:

• “raw_values”: Returns a full set of errors in case of multioutput input.

• “uniform_average”: Errors of all outputs are averaged with uniform weight.

By default, no averaging is done.

Returns:

float or list of n_outputs

The root mean squared error.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_diabetes(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> regressor = Ridge()
>>> report = EstimatorReport(regressor, **split_data)
>>> report.metrics.rmse()
56.5...